NLP To Personalised Medicine: Tech Advances India Has Made For Faster Drug Development

The drug development process has greatly benefited from advancements in Natural Language Processing computational models to develop medicines in the least possible timeline.

The pre-clinical testing phase is often considered the most rigorous stage in the drug development process

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With the rise of technological applications, the clinical research sector has seen several solutions coming up to circumvent challenges such as high development costs, high attrition of candidate molecules, long development timelines, etc. The advancement of technology has also has an impact in the early pre-clinical stages, whether in the identification of molecules or pathways or use of computational models.

The pre-clinical testing phase is often considered the most rigorous stage in the drug development process that takes around 10-12 years to identify thousands of potential drug candidates for a particular disease and finally just two to three of those compounds reach the animal testing phase. This is an important phase of the research where a newly identified chemical entity transits from the laboratory to the animal testing phase as per the regulatory requirements. 

However, the combination of new “tissue-in-a-box” innovations and increased accuracy of computational models is transforming this traditional approach. 

READ | AI, Predictive Analytics, Big Data: How Technology Is Impacting Clinical Research, Drug Trials In India

Many of us rejoiced at the news that came from Alpha DeepFold last year, which demonstrated the capability to generate 3-D fold models in a manner of hours; a process that would generally take weeks if not months. This was one of the latest in a series of technical advancements that held the promise of truly accelerating the pre-clinical phase of drug development.  

We consider a few other examples below:

Use Of NLP (Natural Language Processing) In Literature Search

We are all aware of the distributed nature of research, of how labs across the world continue to publish results on protein binding, of genes that seem “correlated”, etc.  The research in this area is voluminous and it has been challenging to have an efficient method to collate all this research and derive insights from it.  However, with the advancement of NLP techniques, this has become much easier. There are many companies today that offer services in crawling through all the published research and retrieving specific information about the proteins that are of interest.  There are also massive databases created with these relationships, which makes basic research much easier to navigate.

Computational Models To Build Drug Candidates

Developing the right drug entities has traditionally been a very labour-intensive in-vitro process. With advances in computational capacity coupled with a better understanding of the disease at a molecular level, scientists have better capabilities to develop the most suitable new chemical entity that could treat the disease.

Building these models in-silico allows the scientists to consider large number of combinations in a short period of time, thereby allowing the identification of drug candidates in a much shorter period. Today a potential drug molecule could be developed from a living or synthetic material, using high-throughput screening (HTS) techniques, biotechnology or genetically engineered living systems to produce disease-fighting molecules.

Focus On Personalised Medicine

The availability of specific genetic information has transformed the drug development process to focus on drugs that are likely to impact a targetted population with specific genetic traits. This specificity has allowed the development of drugs that have better safety profiles and, thanks to the increased focus on personalised medicine, have a lesser chance of failure of the investigational molecule in comparison to research carried out a decade ago. This has been made possible due to the advancements in high throughput screenings and better computational models.

Pre-Clinical Testing

This is an important phase of the research where a newly identified chemical entity transits from the laboratory to the animal testing phase as per the regulatory requirements. However, the combination of new “tissue-in-a-box” innovations and increased accuracy of computational models is transforming this traditional approach.

One of the most exciting innovations is the creation of “boxes” which mimic functioning tissues.  These slides not only have the tissue from commonly impacted organs such a lung or liver, but they also mimic vascular or respiratory processes, thereby allowing one to simulate the effect of the drug candidate on functioning organs.  

The results from these experiments can then be modelled with much greater accuracy, making it possible to predict efficacy, safety and possible side-effects on human models and provide the most reliable data to the regulatory authorities. This could result in the in-vitro experiments completely replacing the need for testing in animals.

Why Getting A Drug From The 'Lab To The Shelf' Takes Less Time Now

Today, as we are at the intersection between advancements in engineering and biology, further research and process breakthroughs in the field of developing cells and tissues that can cater to more positive outcomes from preclinical studies. With new-age preclinical research as the backbone of drug development, there is a higher chance for success rates of drug candidates that reach the clinical research stage. 

Therefore, with the latest advancements that are available today, the chances of getting a drug from the ”lab to the shelf” in a shorter timeline is more feasible.

As I shared in the previous article, the focus of innovation and technology in the clinical conduct stage was on automation in the data aggregation and analysis areas. In the pre-clinical stage, the focus is on new bio-engineering and advanced computational models that are able to move in-vivo and in-vitro processes to primarily in-silico process. 

Combining these approaches across the different stages of clinical trials will surely lead to a shortened drug-development process with a much higher success rate.

(The author is the VP, GDO India Head & Innovation Head, Parexel India)

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